Real and Artificial Intelligence in Financial Markets

Subject

"Buy low, sell high" is a common-sense way to lose all your money on the stock market. The problem is not in that it's wrong, but in the interpretation. To most people, "buy low" means buying when prices are falling, and "sell high" means selling when prices are rising. Unfortunately, prices fall for only one reason - because the market thinks they are too high - and rise for only one reason - because the market thinks they are too low. So the inexperienced active investor does exactly the opposite of what they should do - buying high and selling low - with inevitable results.

Correct interpretation of this common-sense maxim leads to buying when prices are rising (even if they're at their all-time high) and selling or short-selling when prices are falling (even if they're at their all-time low). This strategy, commonly known as "momentum investing", works in most market conditions, and is at the heart of several trading techniques used by both technical analysts and investmentfunds. Andthefactthatitworks,after25yearsofstudyinthecontextofmodernfinancial theories, remains inexplicable. The Capital Asset Pricing Model assumes investors are trying to maximise returns while minimising risks - a goal which any investor would be wise to try to achieve. Arbitrage Pricing Theory predicts that prices will quickly rise and fall to stable levels due to normal trading activity. Modern Portfolio Theory builds on these theories, to construct optimal return- maximising risk-minimising portfolios. And the Efficient Market Hypothesis states that, as a result of all this rational behaviour, it should be impossible to profit based on nothing but past prices. None of this really gels with the persistent success of momentum investing, which takes no account of risk, makes no attempt to optimise, assumes prices trend slowly, and profits from nothing but the barest minimum of past-price information. This is why finance-theory luminaries Fama and French dubbed it the "premier anomaly".

So what is the reason for this? Many believe the answer is in the first paragraph above - investors aren't rational, and momentum trading profits from their irrationality. The problem with this is that momentum trading has been well documented and well used in one form or another for 25 years. Use should have reduced the time periods - typically 3 to 12 months - over which momentum operates, and this doesn't appear to have happened.

Another possibility is that our theories about how prices are set are too simplistic - that is, they assume that prices have a straight-line relationship with a small number of market factors or stock properties. "Big Data" - the application of statistics, artificial intelligence and machine learning to large volumes of information - can be used to investigate this possibility. Vast amounts of data from sources such as Bloomberg or SIRCA can be analysed using programs written in special-purpose languages such as R in order to find difficult-to-detect relationships, and to develop and test trading strategies.

This approach has its promise and its problems. The promise is a piece of the "holy grail" of algorithmic trading firms the world over - a momentum trading algorithm that learns from the past - which is why so much time and money is being invested in these approaches. The problems arise not just from the technical difficulties, but from the nature of the problem. Big Data is inherently complicated, whereas momentum investing, with its "tiny data" approach, is inherently simple. When using something so complicated to study something so simple, what kind of insights, and what kind of profits, can be made? Time will tell.